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GC x GC-TOFMS and supervised multivariate approaches to study human cadaveric decomposition olfactive signatures

NCJ Number
Pierre-Hughes Stefanuto, Katelynn A. Stadler, Romain Pesesse, Helene N. Leblanc, Shari L. Forbes, Jean-Francois Focant
Date Published
June 2015
12 pages
This study combined the analytical advantages of two-dimensional gas chromatography (GC×GC) coupled to time-of-flight mass spectrometry (TOFMS) with the data handling robustness of supervised multivariate statistics to investigate the volatile organic compounds (VOC) profile of human remains during early stages of decomposition.
The study demonstrated that pig and human decomposition processes can be described by the same trends for the major compounds produced during the early stages of soft tissue decomposition. In forensic thanato-chemistry, the understanding of the process of soft tissue decomposition is still limited. A better understanding of the decomposition process and the characterization of the associated volatile organic compounds (VOC) can help to improve the training of victim recovery (VR) canines, which are used to search for trapped victims in natural disasters or to locate corpses during criminal investigations. The complexity of matrices and the dynamic nature of this process require the use of comprehensive analytical methods for investigation. Moreover, the variability of the environment and between individuals creates additional difficulties in terms of normalization. The resolution of the complex mixture of VOCs emitted by a decaying corpse can be improved using comprehensive two-dimensional gas chromatography (GC× GC), compared to classical single-dimensional gas chromatography (1DGC). In the current study, various supervised multivariate approaches were compared to interpret the large data set. Moreover, early decomposition stages of pig carcasses (typically used as human surrogates in field studies) were also monitored to obtain a direct comparison of the two VOC profiles and estimate the robustness of this human decomposition analog model. (Publisher abstract modified)
Date Published: June 1, 2015